Industry Insights

Key trends in GeoAI Techniques that are Going to Influence the Geospatial Applications

This blog post on Key Trends related to GeoAI Techniques is part of the GeoAI Series, inspired by the WGIC GeoAI Report.

Harsha Vardhan Madiraju July 6, 2021
Based on the current state of AI algorithms, there are three Key Trends related to GeoAI Techniques.

What is GeoAI?

GeoAI is an emerging scientific discipline that combines innovations in spatial science, AI/ML methods (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data.

Based on the state of AI algorithms, most geospatial AI/ML (GeoAI) use-cases over the next two years are likely to fall into the category of descriptive analysis, meaning that the AI models can identify the objects they are looking for and perform statistical analysis on such observations. 

The next phase in the medium term of three to five years is expected to be more of a predictive phase, where the algorithms will be capable of evaluating contextual data (e.g., human geography, mobility, transactions, social media, and sensor data), allowing systems to predict or forecast what to expect. 

The longer-term (five years and beyond) will likely be about prescriptive technologies that recommend specific solutions. This sequence is analogous to Level 1, 2, and 3 of autonomous driving, of the total 5 levels.

The underlying factor contributing to this is the geospatial data, where we see a greater increase in the quality of data, variety, coverage, and frequency. At the same time, the cost of geospatial data is decreasing.

To understand more about this topic, download the WGIC GeoAI report at this link.


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